TL;DR
STF-AL, a sensor-based cross-domain HAR framework that combines spatiotemporal-frequency domain representation learning with adversarial domain adaptation, achieves superior or competitive performance in multiple cross-domain recognition tasks, indicating its effectiveness under the evaluated cross-domain HAR settings.
The results indicate that developing domain-aware alignment and hybrid loss integration techniques is beneficial for effective facial analysis in both controlled and challenging environments.
FDT-PC (Frequency Domain Transformation with Perceptual Constraints), a novel method that enhances adversarial transferability across different model architectures, is proposed, which achieves superior black-box attack performance on both CNNs and Vision Transformers, outperforming existing state-of-the-art input trans...
Bearings are critical components in rotating machinery, and their condition recognition is essential to ensure operational safety and reliability. However, vibration signals collected under different operating scenarios often exhibit non-stationarity and distribution discrepancies, which limits the generalization abili...
Multimodal change detection (CD), due to its ability to flexibly adapt to data acquired from different types of sensors, has become an important research direction in the field of remote sensing. However, existing methods generally lack feature representations with sufficient generalization capacity, leading to pronoun...
Non-visual multimodal embodied perception relies on heterogeneous sensory streams such as inertial signals, physiological measurements, body-worn sensors, ambient sensors, and structured temporal data. Although these modalities provide complementary information for embodied intelligence systems, they also introduce new...
PAPT++ is introduced, a risk-aware adversarial generation-training framework for SDG that progressively exposes the classifier to challenging yet semantically consistent variations.
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